期刊
SIAM JOURNAL ON SCIENTIFIC COMPUTING
卷 35, 期 6, 页码 B1304-B1320出版社
SIAM PUBLICATIONS
DOI: 10.1137/130915406
关键词
structured total least squares; total variation; regularization; alternating minimization; image restoration
资金
- 973 Program [2013CB329404]
- NSFC [61170311, 61370147]
- Chinese Universities Specialized Research Fund for the Doctoral Program [20110185110020]
- Sichuan Province Scientific and Technical Research Project [2012GZX0080]
- RGC GRF [202013]
- HKBU FRG [FRG/12-13/065]
- National Natural Science Foundation of China [11201341]
- China Postdoctoral Science Foundation [2012M511126, 2013T60459]
In this paper, we study the total variation structured total least squares method for image restoration. In the image restoration problem, the point spread function is corrupted by errors. In the model, we study the objective function by minimizing two variables: the restored image and the estimated error of the point spread function. The proposed objective function consists of the data-fitting term containing these two variables, the magnitude of error and the total variation regularization of the restored image. By making use of the structure of the objective function, an efficient alternating minimization scheme is developed to solve the proposed model. Numerical examples are also presented to demonstrate the effectiveness of the proposed model and the efficiency of the numerical scheme.
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